HAVIC Pilot Transcription
|Item Name:||HAVIC Pilot Transcription|
|Author(s):||Jennifer Tracey, Stephanie Strassel, Amanda Morris, Xuansong Li, Brian Antonishek, Jonathan G. Fiscus|
|LDC Catalog No.:||LDC2016V01|
|Release Date:||April 18, 2016|
|DCMI Type(s):||MovingImage, Text|
|Data Source(s):||web collection|
LDC User Agreement for Non-Members
|Online Documentation:||LDC2016V01 Documents|
|Licensing Instructions:||Subscription & Standard Members, and Non-Members|
|Citation:||Tracey, Jennifer, et al. HAVIC Pilot Transcription LDC2016V01. Web Download. Philadelphia: Linguistic Data Consortium, 2016.|
|Related Works: Hide||View|
HAVIC Pilot Transcription was developed by the Linguistic Data Consortium (LDC) and is comprised of approximately 72 hours of user-generated videos with transcripts based on the English speech audio extracted from the videos. This data set was created in collaboration with NIST (the National Institute of Standards and Technology) as part of the HAVIC (the Heterogeneous Audio Visual Internet Collection) project, the goal of which was to advance multimodal event detection and related technologies.
LDC has developed a large, heterogeneous, annotated multimodal corpus for HAVIC that has been used in the NIST-sponsored MED (Multimedia Event Detection) task for several years. HAVIC Pilot Transcription supported an experiment to produce a verbatim transcript (quick and rich transcription) based on audio extracted from user-generated videos. It contains the pilot transcripts for selected MED 2011 video files as well as the associated videos.
NIST designated the videos to be transcribed. Annotators generated the transcripts using XTrans, which supports manual transcription across multiple channels, languages and platforms. HAVIC transcription guidelines are included in the documentation for this release.
Each file was transcribed by a single annotator with no corpus-wide second pass. File samples from each annotator were checked for various errors, including missing transcription, improper mark-up, poor segmentation and missing/added words.
All transcription files are in .tdf format, a plain-text, flat-table format with 13 tab-delimited fields. All video files are in .mp4 format (h264), with varying bit-rates and levels of audio fidelity and video resolution.
None at this time.